Beaufort Gyre freshwater reservoir: State and variability from observations
Bibliographic record
Abstract
We investigate basin‐scale mechanisms regulating anomalies in freshwater content (FWC) in the Beaufort Gyre (BG) of the Arctic Ocean using historical observations and data collected in 2003–2007. Specifically, the mean annual cycle and interannual and decadal FWC variability are explored. The major cause of the large FWC in the BG is the process of Ekman pumping (EP) due to the Arctic High anticyclonic circulation centered in the BG. The mean seasonal cycle of liquid FWC is a result of interplay between the mechanical (EP) and thermal (ice transformations) factors and has two peaks. One peak occurs around June–July when the sea ice thickness reaches its minimum (maximum ice melt). The second maximum is observed in November–January when wind curl is strongest (maximum EP) and the salt input from the growing ice has not yet reached its maximum. Interannual changes in FWC during 2003–2007 are characterized by a strong positive trend in the region varying by location with a maximum of approximately 170 cm a−1 in the center of EP influenced region. Decadal FWC variability in the period 1950–2000 is dominated by a significant change in the 1990s forced by an atmospheric circulation regime change. The center of maximum FWC shifted to the southeast and appeared to contract in area relative to the pre‐1990s climatology. In spite of the areal reduction, the spatially integrated FWC increased by over 1000 km3 relative to climatology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".